Translator Disclaimer
Paper
16 December 1999 Trends in lossless image compression: adaptive vs. classified prediction and context modeling for entropy coding
Author Affiliations +
Abstract
This paper discusses the most recent trends in the reversible intraframe compression of grayscale images. With reference to a spatial DPCM scheme, prediction, either linar or nonlinear, may be accomplished in a space-varying fashion following two main strategies: adaptive, i.e., with predictors recalculated at each pixel position, and classified, in which image blocks, or pixels are preliminarily labeled into a number of statistical classes, for which optimum MMSE predictors are calculated. A trade- off between the above two strategies is proposed. It relies on a classified linear-regression prediction obtained through fuzzy techniques, followed by context-based modeling of the outcome prediction errors, to enhance entropy coding. The present scheme is a reworking of a fuzzy encode previously presented by the authors. Now, predictors, instead of pixel intensity patterns, are fuzzy-clustered to find out optimized MMSE prediction classes, and a novel membership function measuring the fitness of prediction is adopted. A thorough performances comparison with the most advanced methods in the literature highlights advantages, and drawbacks as well, of the fuzzy approach.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Bruno Aiazzi, Luciano Alparone, and Stefano Baronti "Trends in lossless image compression: adaptive vs. classified prediction and context modeling for entropy coding", Proc. SPIE 3814, Mathematics of Data/Image Coding, Compression, and Encryption II, (16 December 1999); https://doi.org/10.1117/12.372744
PROCEEDINGS
11 PAGES


SHARE
Advertisement
Advertisement
Back to Top